Lipid Droplets and Their Autophagic Turnover via the Raft-Like Vacuolar Microdomains

Although once perceived as inert structures that merely serve for lipid storage, lipid droplets (LDs) have proven to be the dynamic organelles that hold many cellular functions. The LDs’ basic structure of a hydrophobic core consisting of neutral lipids and enclosed in a phospholipid monolayer allows for quick lipid accessibility for intracellular energy and membrane production. Whereas formed at the peripheral and perinuclear endoplasmic reticulum, LDs are degraded either in the cytosol by lipolysis or in the vacuoles/lysosomes by autophagy. Autophagy is a regulated breakdown of dysfunctional, damaged, or surplus cellular components. The selective autophagy of LDs is called lipophagy. Here, we review LDs and their degradation by lipophagy in yeast, which proceeds via the micrometer-scale raft-like lipid domains in the vacuolar membrane. These vacuolar microdomains form during nutrient deprivation and facilitate internalization of LDs via the vacuolar membrane invagination and scission. The resultant intra-vacuolar autophagic bodies with LDs inside are broken down by vacuolar lipases and proteases. This type of lipophagy is called microlipophagy as it resembles microautophagy, the type of autophagy when substrates are sequestered right at the surface of a lytic compartment. Yeast microlipophagy via the raft-like vacuolar microdomains is a great model system to study the role of lipid domains in microautophagic pathways.

Characterizing the lipid-protein interface of the human serotonin transporter by crosslinking mass spectrometry

ABSTRACTAltered serotonin (5-HT) levels contribute to disease states such as depression and anxiety. Synaptic serotonin concentration is partially regulated by the serotonin transporter (SERT), making this transporter an important therapeutic target. This study seeks to examine the lipid accessible domains of hSERT to provide critical information regarding the apo-state of this transporter in a lipid environment. Recombinant hSERT was inducibly expressed in a human cell line. Solubilized SERT was purified by affinity chromatography using a FLAG Tag and reconstituted into mixed lipid vesicles containing our photoactivatable lipid probe. The lipid-accessible domains of the reconstituted transporter in membranes in its apo-state were probed via photocrosslinking to azi-cholesterol followed by quadrupole time of flight liquid chromatography-mass spectrometry (QTOF-LC-MS). MS studies identified crosslinks in three transmembrane loops consistent with the known topology of SERT. Surprisingly, the amino- and carboxy-terminal domains were similarly crosslinked by cholesterol, suggesting that these regions may also be intimately associated with the lipid bilayer. The data presented herein assist in further refining our understanding of the topography of the apo-state of hSERT via analysis of lipid accessibility.

Computing structure-based lipid accessibility of membrane proteins with mp_lipid_acc in RosettaMP

BackgroundMembrane proteins are vastly underrepresented in structural databases, which has led to a lack of computational tools and the corresponding inappropriate use of tools designed for soluble proteins. For membrane proteins, lipid accessibility is an essential property. Even though programs are available for sequence-based prediction of lipid accessibility and structure-based identification of solvent-accessible surface area, the latter does not distinguish between water accessible and lipid accessible residues in membrane proteins.ResultsHere we present mp_lipid_acc, the first method to identify lipid accessible residues from the protein structure, implemented in the RosettaMP framework and available as a webserver. Our method uses protein structures transformed in membrane coordinates, for instance from PDBTM or OPM databases, and a defined membrane thickness to classify lipid accessibility of residues. mp_lipid_acc is applicable to both α-helical and β-barrel membrane proteins of diverse architectures with or without water-filled pores and uses a concave hull algorithm for classification. We further provide a manually curated benchmark dataset, on which our method achieves prediction accuracies of 90%.ConclusionWe present a novel tool to classify lipid accessibility from the protein structure, which is applicable to proteins of diverse architectures and achieves prediction accuracies of 90% on a manually curated database. mp_lipid_acc is part of the Rosetta software suite, available at www.rosettacommons.org. The webserver is available at http://rosie.graylab.jhu.edu/mp_lipid_acc/submit and the benchmark dataset is available at http://tinyurl.com/mp-lipid-acc-dataset.Supplementary informationSupplementary information is available at BMC Bioinformatics.